Location-based channel estimation in wireless communication systems

ABSTRACT

Systems, methods, and devices to reduce the channel estimation overhead by collecting data from many UEs and building a location-based mathematical model are disclosed. During building of the model, a reference signal is used to collect location- and signal-related data from connected UEs. Once the model is successfully built, it is then transmitted and/or downloaded to each new UE that connects to the base station. The UEs and/or the base stations then use this model to determine their own transmission parameter values. The UEs also report their location to the base stations, which use the model to estimate channel conditions and adapt transmission parameters for themselves.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. Pat. Application No.17/680,193, filed on Feb. 24, 2022, entitled LOCATION-BASED CHANNELESTIMATION IN WIRELESS COMMUNICATION SYSTEMS, which is herebyincorporated by reference in its entirety.

BACKGROUND

Channel estimation plays a very important role in the performance ofwireless communication systems.

BRIEF DESCRIPTION OF THE DRAWINGS

Detailed descriptions of implementations of the present invention willbe described and explained through the use of the accompanying drawings.

FIG. 1 is a block diagram that illustrates a wireless communicationssystem.

FIG. 2 illustrates an example call flow for channel estimation modelcreation.

FIG. 3 illustrates an example call flow for channel estimation modelusage.

FIG. 4A is a flow chart representation of a method for wirelesscommunication in accordance with one or more embodiments of the presenttechnology.

FIG. 4B is a flow chart representation of a method for wirelesscommunication in accordance with one or more embodiments of the presenttechnology.

FIG. 5 is a flow chart representation of a method for wirelesscommunication in accordance with one or more embodiments of the presenttechnology.

FIG. 6 is a block diagram that illustrates an example of a computersystem in which at least some operations described herein can beimplemented.

FIG. 7 illustrates an example model used to determine channeltransmission parameters in accordance with one or more embodiments ofthe present technology.

The technologies described herein will become more apparent to thoseskilled in the art from studying the Detailed Description in conjunctionwith the drawings. Embodiments or implementations describing aspects ofthe invention are illustrated by way of example, and the same referencescan indicate similar elements. While the drawings depict variousimplementations for the purpose of illustration, those skilled in theart will recognize that alternative implementations can be employedwithout departing from the principles of the present technologies.Accordingly, while specific implementations are shown in the drawings,the technology is amenable to various modifications.

DETAILED DESCRIPTION

In wireless cellular communication systems, the quality of signalreceived by a user equipment (UE, e.g., a mobile phone) or a basestation (BS) heavily depends on the physical location of the UE. Hence,when the UE is mobile, frequent estimation of signal quality is needed.This estimation (also known as channel estimation) is generallyperformed based on a specific signal (also known as a reference signal).

In one method, the reference signal can be transmitted by the basestation and received by the UE. The UE measures the power of thereference signal (or signal-to-noise-ratio (SNR) orsignal-to-interference-and-noise-ratio (SINR)) and sends the measuredvalue back to the base station. The base station uses this value toestimate the radio channel conditions that the UE is experiencing. Basedon this estimation, the base station determines optimal values ofseveral transmission parameters (such as modulation schemes,multiple-input and multiple-output (MIMO) rank, and so on). Values ofthese parameters are then sent to the UE, which adapts its subsequenttransmitted signal accordingly.

In another method, the UE sends a reference signal to the base station,which measures its power level (or SNR/ SINR) and reports back to theUE. Both the UE and the base station determine optimal values of thetransmission parameters according to the measurement report.

This process is repeated periodically and frequently for every UEconnected to the base station and each time new channel estimation isperformed. These methods create a lot of overhead in that they use partof a frequency band (spectrum) and time slots for channel estimation andnot for payload. This results in poor utilization of the spectrum, thatis, low spectral efficiency. As a result, conventional methods ofchannel estimation consume extensive wireless resources, and also resultin a degraded user experience due to reduced data throughput.

To overcome these and other deficiencies of conventional systems, theinventor has conceived and reduced to practice systems and methods toreduce the channel estimation overhead by collecting data from many UEsand building a location-based mathematical model. During building of themodel, a reference signal can be used as in the conventional systems.However, once the model is successfully built, it is then transmittedand/or downloaded to each new UE that connects to the base station. TheUEs and/or the base stations then use this model to determine their owntransmission parameter values. The UEs also report their location to thebase station which uses the model to estimate channel conditions andadapt transmission parameters for themselves. In this manner, areference signal as in conventional systems is no longer used forchannel estimation or is only sporadically used. Hence the spectrum andtime slots used by the reference signal are freed up to be used forpayload. This increases data throughput and spectral efficiency, whichin turn increases the value of telecommunication assets by obtainingmore data throughput from each MHz. It also improves user experiencebecause more bandwidth is made available for data use and transmissionrather than for channel estimation-related payload.

Wireless Communications System

FIG. 1 is a block diagram that illustrates a wireless telecommunicationsystem 100 (“system 100”) in which aspects of the disclosed technologyare incorporated. The system 100 includes base stations 102-1 through102-4 (also referred to individually as “base station 102” orcollectively as “base stations 102”). A base station is a type ofnetwork access node (NAN) that can also be referred to as a cell site, abase transceiver station, or a radio base station. The system 100 caninclude any combination of NANs including an access point, radiotransceiver, gNodeB (gNB), NodeB, eNodeB (eNB), Home NodeB or eNodeB, orthe like. In addition to being a wireless wide area network (WWAN) basestation, a NAN can be a wireless local area network (WLAN) access point,such as an Institute of Electrical and Electronics Engineers (IEEE)802.11 access point.

The NANs of a network formed by the system 100 also include wirelessdevices 104-1 through 104-7 (referred to individually as “wirelessdevice 104” or collectively as “wireless devices 104”) and a corenetwork 106. The wireless devices 104-1 through 104-7 can correspond toor include network entities capable of communication using variousconnectivity standards. For example, a 5G communication channel can usemillimeter wave (mmW) access frequencies of 28 GHz or more. In someimplementations, the wireless device 104 can operatively couple to abase station 102 over a long-term evolution (LTE)/LTE advanced (LTE-A)communication channel, which is referred to as a 4G communicationchannel. In some implementations, the base station 102 can providenetwork access to a fifth-generation (5G) communication channel.

The core network 106 provides, manages, and controls security services,user authentication, access authorization, tracking, Internet Protocol(IP) connectivity, and other access, routing, or mobility functions. Thebase stations 102 interface with the core network 106 through a firstset of backhaul links 108 (e.g., S1 interfaces) and can perform radioconfiguration and scheduling for communication with the wireless devices104 or can operate under the control of a base station controller (notshown). In some examples, the base stations 102 can communicate, eitherdirectly or indirectly (e.g., through the core network 106), with eachother over a second set of backhaul links 110-1 through 110-3 (e.g., X1interfaces), which can be wired or wireless communication links.

The base stations 102 can wirelessly communicate with the wirelessdevices 104 via one or more base station antennas. The cell sites canprovide communication coverage for geographic coverage areas 112-1through 112-4 (also referred to individually as “coverage area 112” orcollectively as “coverage areas 112”). The geographic coverage area 112for a base station 102 can be divided into sectors making up only aportion of the coverage area (not shown). The system 100 can includebase stations of different types (e.g., macro and/or small cell basestations). In some implementations, there can be overlapping geographiccoverage areas 112 for different service environments (e.g.,Internet-of-Things (IoT), mobile broadband (MBB), vehicle-to-everything(V2X), machine-to-machine (M2M), machine-to-everything (M2X),ultra-reliable low-latency communication (URLLC), machine-typecommunication (MTC)), etc. The base stations 102 can host the channelestimation model(s) at, for example, local and/or remote memorylocations.

The system 100 can include a 5G network and/or an LTE/LTE-A or othernetwork. In an LTE/LTE-A network, the term eNB is used to describe thebase stations 102, and in 5G new radio (NR) networks, the term gNBs isused to describe the base stations 102 that can include mmWcommunications. The system 100 can thus form a heterogeneous network inwhich different types of base stations provide coverage for variousgeographical regions. For example, each base station 102 can providecommunication coverage for a macro cell, a small cell, and/or othertypes of cells. As used herein, the term “cell” can relate to a basestation, a carrier or component carrier associated with the basestation, or a coverage area (e.g., sector) of a carrier or base station,depending on context.

A macro cell generally covers a relatively large geographic area (e.g.,several kilometers in radius) and can allow access by wireless deviceswith service subscriptions with a wireless network service provider. Asindicated earlier, a small cell is a lower-powered base station, ascompared with a macro cell, and can operate in the same or different(e.g., licensed, unlicensed) frequency bands as macro cells. Examples ofsmall cells include pico cells, femto cells, and micro cells. Ingeneral, a pico cell can cover a relatively smaller geographic area andcan allow unrestricted access by wireless devices with servicesubscriptions with the network provider. A femto cell covers arelatively smaller geographic area (e.g., a home) and can providerestricted access by wireless devices having an association with thefemto cell (e.g., wireless devices in a closed subscriber group (CSG),wireless devices for users in the home). A base station can support oneor multiple (e.g., two, three, four, and the like) cells (e.g.,component carriers). All fixed transceivers noted herein that canprovide access to the network are NANs, including small cells.

The communication networks that accommodate various disclosed examplescan be packet-based networks that operate according to a layeredprotocol stack. In the user plane, communications at the bearer orPacket Data Convergence Protocol (PDCP) layer can be IP-based. A RadioLink Control (RLC) layer then performs packet segmentation andreassembly to communicate over logical channels. A Medium Access Control(MAC) layer can perform priority handling and multiplexing of logicalchannels into transport channels. The MAC layer can also use Hybrid ARQ(HARQ) to provide retransmission at the MAC layer, to improve linkefficiency. In the control plane, the Radio Resource Control (RRC)protocol layer provides establishment, configuration, and maintenance ofan RRC connection between a wireless device 104 and the base stations102 or core network 106 supporting radio bearers for the user planedata. At the Physical (PHY) layer, the transport channels are mapped tophysical channels.

As illustrated, the wireless devices 104 are distributed throughout thesystem 100, where each wireless device 104 can be stationary or mobile.A wireless device can be referred to as a mobile station, a subscriberstation, a mobile unit, a subscriber unit, a wireless unit, a remoteunit, a handheld mobile device, a remote device, a mobile subscriberstation, an access terminal, a mobile terminal, a wireless terminal, aremote terminal, a handset, a mobile client, a client, or the like.Examples of a wireless device include user equipment (UE) such as amobile phone, a personal digital assistant (PDA), a wireless modem, ahandheld mobile device (e.g., wireless devices 104-1 and 104-2), atablet computer, a laptop computer (e.g., wireless device 104-3), and awearable (e.g., wireless device 104-4). A wireless device can beincluded in another device such as, for example, a drone (e.g., wirelessdevice 104-5), a vehicle (e.g., wireless device 104-6), an augmentedreality/virtual reality (AR/VR) device such as a head-mounted displaydevice (e.g., wireless device 104-7), an IoT device such as an appliancein a home, a portable gaming console, or a wirelessly connected sensorthat provides data to a remote server over a network. A wireless devicecan use various techniques to determine its location, such as anon-board global positioning system (GPS) (e.g., which provides x,ycoordinates and an altitude meter to provide a z coordinate). While notnecessary, it is useful to know a wireless device’s location in the zcoordinate (i.e., height above ground) since that can also have aneffect on signal quality. The wireless device also has a memory to storeone or more channel models received from a base station.

A wireless device can communicate with various types of base stationsand network equipment at the edge of a network including macroeNBs/gNBs, small cell eNBs/gNBs, relay base stations, and the like. Awireless device can also communicate with other wireless devices eitherwithin or outside the same coverage area of a base station viadevice-to-device (D2D) communications.

The communication links 114-1 through 114-10 (also referred toindividually as “communication link 114” or collectively as“communication links 114”) shown in system 100 include uplink (UL)transmissions from a wireless device 104 to a base station 102, and/ordownlink (DL) transmissions from a base station 102 to a wireless device104. The downlink transmissions can also be called forward linktransmissions while the uplink transmissions can also be called reverselink transmissions. Each communication link 114 includes one or morecarriers, where each carrier can be a signal composed of multiplesub-carriers (e.g., waveform signals of different frequencies) modulatedaccording to the various radio technologies. Each modulated signal canbe sent on a different sub-carrier and carry control information (e.g.,reference signals, control channels), overhead information, user data,etc. The communication links 114 can transmit bidirectionalcommunications using frequency division duplex (FDD) (e.g., using pairedspectrum resources) or time division duplex (TDD) operation (e.g., usingunpaired spectrum resources). In some implementations, the communicationlinks 114 include LTE and/or mmW communication links.

In some implementations of the system 100, the base stations 102 and/orthe wireless devices 104 include multiple antennas for employing antennadiversity schemes to improve communication quality and reliabilitybetween base stations 102 and wireless devices 104. Additionally, oralternatively, the base stations 102 and/or the wireless devices 104 canemploy multiple-input and multiple-output (MIMO) techniques that cantake advantage of multi-path environments to transmit multiple spatiallayers carrying the same or different coded data.

Location-Based Channel Estimation - Model Creation

FIG. 2 illustrates an example call flow 200 for channel estimation modelcreation. Once a UE (202 a,..., 202 n) is connected to a network 205 viaa base station, it sends its location information, such as GPScoordinates, in every transmission or data packet 210 a,..., 210 n,along with the power measurement report (or SNR, SINR) of the referencesignal. The network 205 (e.g., via the base station) collects thatinformation from multiple UEs 202 a,..., 202 n that pass through thatphysical location, correlates their power measurement reports to thatphysical location, and formulates a set of mathematical models (such asa statistical distribution, trained machine learning model, and so on)of the signal attenuation parameters, such as power, signal strength,SNR, SINR, reference signal received power (RSRP), reference signalstrength indication (RSSI), and so on. The network 205 can create (at220) one or more models for each of the signal attenuation parameters.The model(s) can then be used to estimate values for correspondingchannel transmission parameter(s) at the UEs and/or the base station.Examples of channel transmission parameters include, but are not limitedto, modulation scheme, multiple-input and multiple-output (MIMO) rank,transmit power level, channel bandwidth, number of carriers, number ofsub-carriers, sub-carrier spacing, and so on.

For example, the network 205 can generate a model illustrated in FIG. 7for a UE location (x1, y1, z1). The model indicates that the SINR ismost likely to be around 0 dB at this location for any given UE. Hence,this is the value that should be used by the UE or the base station todetermine the channel transmission parameters, such as MIMO rank andmodulation scheme.

In some implementations, the network 205 generates separate models foruplink and downlink directions. For example, the model can be the samefor uplink and downlink (such as in TDD systems) or different for uplinkand downlink (such as in FDD systems).

In some implementations, the network 205 generates separate models fordifferent bands (e.g., a set of models for the 600 MHz band and a set ofmodels for the 3 GHz systems) of base stations. In this manner, thedisclosed systems and methods can address attenuation and othersignal-dampening problems for different frequency bands. Network 205 canstore the models in one or more data storage locations remotelyaccessible by the various base stations. For ease of retrieval andusage, the models can be associated with identifiers for one or morebase stations with which they are associated. In some implementations,the models are stored in data storage locations locally accessible byspecific base stations. The models can be refreshed/recomputed atperiodic intervals (e.g., monthly, quarterly, etc.), when specificevents occur (e.g., a new telecommunications service is rolled out),when changes are made to the telecommunications network (e.g., a celltower is added/removed), and so on.

FIG. 4A is a flow chart representation of a process 400 for wirelesscommunication performed by, for example, a node (e.g., base station) ina telecommunications network. At acts 410 and 420, the network nodereceives a current location for a UE and associated path lossinformation in the form of one or more signal attenuation parametervalues. At block 430, process 400 determines whether it has receivedsufficient path loss information for a location (e.g., a base stationcoverage area, a particular location (e.g., represented as x-y-zcoordinates), an area surrounding a particular location (e.g., a 10square mile radius area surrounding a location), and so on) so that aset of models can be generated. Process 400 performs acts 410-430 untilsufficient location-path loss information is available. When sufficientlocation-path loss information is available, process 400 proceeds to act440 where it generates one or more models, as discussed above.

Location-Based Channel Estimation - Model Usage

FIG. 3 illustrates an example call flow for channel estimation modeltransfer and usage. When a UE 202 attaches to a network 205 (e.g., usingan attachment notification to connect to a base station 305), thenetwork 205 determines the base station and/or frequency band forcommunications between the UE 202 and the network 205. Based on theidentified base station and/or frequency band, and/or type of UE thenetwork 205 identifies one or more models applicable for the UE 202. Forexample, when it is determined that communications between the UE 202and a base station of the network 205 will occur over the 3 GHzfrequency band, network 205 can select a set of models corresponding tothe 3 GHz frequency band. As another example, during carrieraggregation/dual connectivity, when it is determined that communicationsbetween the UE 202 and a base station of the network 205 will occur overboth the low band and mid band, the network selects two sets of models,each corresponding to the two frequency bands.

Additionally, the network 205 can select models for some, but not all,of the signal attenuation parameters based upon, for example, thefeatures of the UE, features of the base station, type of communicationsbeing supported, and so on. After the model(s) are identified, they arethen made available to any UE that enters the base station’s coveragearea and connects to it. For example, the network 205 transmits one ormore channel estimation model packets 310 that comprise the identifiedmodel(s). As another example, the models are stored at a data storagelocation communicatively coupled to the terminal device, and theterminal device is configured to access the identified subset of modelsfrom the data storage location using the information in the receivedpacket (e.g., model identifiers). In some implementations, instead ofselecting a subset of models, all models for the base stations are madeavailable to the UE.

The model(s) are then used by the UE and/or the base station to estimatechannel parameters at that particular location of the UE without goingthrough the cumbersome channel estimation process used in conventionalsystems. This reduces the overhead and increases communicationefficiency over that frequency channel, also known as increased spectralefficiency.

FIG. 4B is a flow chart representation of a process 450 for wirelesscommunication performed at a UE in a telecommunications network. Process450 begins at act 460 when a UE attaches to a network node (e.g., basestation). At act 465, process 450 determines whether a model isavailable for the UE-specific attachment (for example, for a frequencyband over which communications between the UE and the network willoccur). At act 470, process 450 identifies the set of models for the UEand transmits one or more of the identified models to the UE at act 475.

FIG. 5 is a flow chart representation of a process 500 for wirelesscommunication performed at a UE in accordance with one or moreembodiments of the present technology. At act 510, a UE attaches to thenetwork. At act 520, the UE accesses one or more UE-specific attachmentmodel(s). Upon receiving the model(s), the UE proceeds to act 530 whereit modifies its behavior by, for example, modifying the values of one ormore channel transmission parameters before transmitting a message tothe network.

Computer System

FIG. 6 is a block diagram that illustrates an example of a computersystem 600 in which at least some operations described herein can beimplemented. As shown, the computer system 600 can include: one or moreprocessors 602, main memory 606, non-volatile memory 610, a networkinterface device 612, a video display device 618, an input/output device620, a control device 622 (e.g., keyboard and pointing device), a driveunit 624 that includes a storage medium 626, and a signal generationdevice 630 that are communicatively connected to a bus 616. The bus 616represents one or more physical buses and/or point-to-point connectionsthat are connected by appropriate bridges, adapters, or controllers.Various common components (e.g., cache memory) are omitted from FIG. 6for brevity. Instead, the computer system 600 is intended to illustratea hardware device on which components illustrated or described relativeto the examples of the figures and any other components described inthis specification can be implemented.

The computer system 600 can take any suitable physical form. Forexample, the computer system 600 can share a similar architecture asthat of a server computer, personal computer (PC), tablet computer,mobile telephone, game console, music player, wearable electronicdevice, network-connected (“smart”) device (e.g., a television or homeassistant device), AR/VR systems (e.g., head-mounted display), or anyelectronic device capable of executing a set of instructions thatspecify action(s) to be taken by the computer system 600. In someimplementations, the computer system 600 can be an embedded computersystem, a system-on-chip (SOC), a single-board computer system (SBC), ora distributed system such as a mesh of computer systems, or it caninclude one or more cloud components in one or more networks. Whereappropriate, one or more computer systems 600 can perform operations inreal time, near real time, or in batch mode.

The network interface device 612 enables the computer system 600 tomediate data in a network 614 with an entity that is external to thecomputer system 600 through any communication protocol supported by thecomputer system 600 and the external entity. Examples of the networkinterface device 612 include a network adapter card, a wireless networkinterface card, a router, an access point, a wireless router, a switch,a multilayer switch, a protocol converter, a gateway, a bridge, a bridgerouter, a hub, a digital media receiver, and/or a repeater, as well asall wireless elements noted herein.

The memory (e.g., main memory 606, non-volatile memory 610,machine-readable medium 626) can be local, remote, or distributed.Although shown as a single medium, the machine-readable medium 626 caninclude multiple media (e.g., a centralized/distributed database and/orassociated caches and servers) that store one or more sets ofinstructions 628. The machine-readable (storage) medium 626 can includeany medium that is capable of storing, encoding, or carrying a set ofinstructions for execution by the computer system 600. Themachine-readable medium 626 can be non-transitory or comprise anon-transitory device. In this context, a non-transitory storage mediumcan include a device that is tangible, meaning that the device has aconcrete physical form, although the device can change its physicalstate. Thus, for example, non-transitory refers to a device remainingtangible despite this change in state.

Although implementations have been described in the context of fullyfunctioning computing devices, the various examples are capable of beingdistributed as a program product in a variety of forms. Examples ofmachine-readable storage media, machine-readable media, orcomputer-readable media include recordable-type media such as volatileand non-volatile memory devices 610, removable flash memory, hard diskdrives, optical disks, and transmission-type media such as digital andanalog communication links.

In general, the routines executed to implement examples herein can beimplemented as part of an operating system or a specific application,component, program, object, module, or sequence of instructions(collectively referred to as “computer programs”). The computer programstypically comprise one or more instructions (e.g., instructions 604,608, 628) set at various times in various memory and storage devices incomputing device(s). When read and executed by the processor 602, theinstruction(s) cause the computer system 600 to perform operations toexecute elements involving the various aspects of the disclosure.

FIG. 7 illustrates an example model used to determine channeltransmission parameters in accordance with one or more embodiments ofthe present technology.

Remarks

The description and associated drawings are illustrative examples andare not to be construed as limiting. This disclosure provides certaindetails for a thorough understanding and enabling description of theseexamples. One skilled in the relevant technology will understand,however, that the invention can be practiced without many of thesedetails. Likewise, one skilled in the relevant technology willunderstand that the invention can include well-known structures orfeatures that are not shown or described in detail, to avoidunnecessarily obscuring the descriptions of examples.

The terms “example,” “embodiment,” and “implementation” are usedinterchangeably. For example, references to “one example” or “anexample” in the disclosure can be, but not necessarily are, referencesto the same implementation; and, such references mean at least one ofthe implementations. The appearances of the phrase “in one example” arenot necessarily all referring to the same example, nor are separate oralternative examples mutually exclusive of other examples. A feature,structure, or characteristic described in connection with an example canbe included in another example of the disclosure. Moreover, variousfeatures are described which can be exhibited by some examples and notby others. Similarly, various requirements are described which can berequirements for some examples but not for other examples.

The terminology used herein should be interpreted in its broadestreasonable manner, even though it is being used in conjunction withcertain specific examples of the invention. The terms used in thedisclosure generally have their ordinary meanings in the relevanttechnical art, within the context of the disclosure, and in the specificcontext where each term is used. A recital of alternative language orsynonyms does not exclude the use of other synonyms. Specialsignificance should not be placed upon whether or not a term iselaborated or discussed herein. The use of highlighting has no influenceon the scope and meaning of a term. Further, it will be appreciated thatthe same thing can be said in more than one way.

Unless the context clearly requires otherwise, throughout thedescription and the claims, the words “comprise,” “comprising,” and thelike are to be construed in an inclusive sense, as opposed to anexclusive or exhaustive sense; that is to say, in the sense of“including, but not limited to.” As used herein, the terms “connected,”“coupled,” and any variants thereof mean any connection or coupling,either direct or indirect, between two or more elements; the coupling orconnection between the elements can be physical, logical, or acombination thereof. Additionally, the words “herein,” “above,” “below,”and words of similar import can refer to this application as a whole andnot to any particular portions of this application. Where contextpermits, words in the above Detailed Description using the singular orplural number may also include the plural or singular numberrespectively. The word “or” in reference to a list of two or more itemscovers all of the following interpretations of the word: any of theitems in the list, all of the items in the list, and any combination ofthe items in the list. The term “module” refers broadly to softwarecomponents, firmware components, and/or hardware components.

While specific examples of technology are described above forillustrative purposes, various equivalent modifications are possiblewithin the scope of the invention, as those skilled in the relevant artwill recognize. For example, while processes or blocks are presented ina given order, alternative implementations can perform routines havingsteps, or employ systems having blocks, in a different order, and someprocesses or blocks may be deleted, moved, added, subdivided, combined,and/or modified to provide alternative or sub-combinations. Each ofthese processes or blocks can be implemented in a variety of differentways. Also, while processes or blocks are at times shown as beingperformed in series, these processes or blocks can instead be performedor implemented in parallel, or can be performed at different times.Further, any specific numbers noted herein are only examples such thatalternative implementations can employ differing values or ranges.

Details of the disclosed implementations can vary considerably inspecific implementations while still being encompassed by the disclosedteachings. As noted above, particular terminology used when describingfeatures or aspects of the invention should not be taken to imply thatthe terminology is being redefined herein to be restricted to anyspecific characteristics, features, or aspects of the invention withwhich that terminology is associated. In general, the terms used in thefollowing claims should not be construed to limit the invention to thespecific examples disclosed herein, unless the above DetailedDescription explicitly defines such terms. Accordingly, the actual scopeof the invention encompasses not only the disclosed examples, but alsoall equivalent ways of practicing or implementing the invention underthe claims. Some alternative implementations can include additionalelements to those implementations described above or include fewerelements.

Any patents and applications and other references noted above, and anythat may be listed in accompanying filing papers, are incorporatedherein by reference in their entireties, except for any subject matterdisclaimers or disavowals, and except to the extent that theincorporated material is inconsistent with the express disclosureherein, in which case the language in this disclosure controls. Aspectsof the invention can be modified to employ the systems, functions, andconcepts of the various references described above to provide yetfurther implementations of the invention.

To reduce the number of claims, certain implementations are presentedbelow in certain claim forms, but the applicant contemplates variousaspects of an invention in other forms. For example, aspects of a claimcan be recited in a means-plus-function form or in other forms, such asbeing embodied in a computer-readable medium. A claim intended to beinterpreted as a means-plus-function claim will use the words “meansfor.” However, the use of the term “for” in any other context is notintended to invoke a similar interpretation. The applicant reserves theright to pursue such additional claim forms either in this applicationor in a continuing application.

We claim:
 1. A system comprising: at least one hardware processor; andat least one non-transitory memory storing instructions, which, whenexecuted by the at least one hardware processor, cause the system to:obtain multiple models indicating one or more signal attenuationparameters, wherein each model is used to estimate values for acorresponding channel transmission parameter at a one or more UEs or abase station in communication with the one or more UEs; receive, from aUE, an attachment notification to connect to the base station; identifya subset of models from the multiple models for the UE; and transmit, tothe UE, a packet comprising information of the identified subset ofmodels.
 2. The system of claim 1, wherein at least one channeltransmission parameter of the packet comprising the information of theidentified subset of models is modified before transmitting the packetto the UE.
 3. The system of claim 1, wherein the multiple models isstored at a data storage location communicatively coupled to the basestation.
 4. The system of claim 1, wherein the multiple models is storedat a data storage location communicatively coupled to the UE, andwherein the UE is configured to access the identified subset of modelsfrom the data storage location using the information in the receivedpacket.
 5. The system of claim 1, wherein the information of theidentified subset of models enables the UE to modify channeltransmission parameter values of communication between the UE and thebase station.
 6. The system of claim 1, wherein the one or more signalattenuation parameters comprise: power, signal strength, signal to noiseratio, signal to interference and noise ratio, reference signal receivedpower, reference signal strength indication, or any combination thereof.7. The system of claim 1, wherein the channel transmission parametercomprises: modulation scheme, multiple-input and multiple-output rank,transmit power level, channel bandwidth, number of carriers, number ofsub-carriers, sub-carrier spacing, or any combination thereof.
 8. Thesystem of claim 1, wherein the multiple models comprises a first subsetof models for uplink communication direction and a second subset ofmodels for downlink communication direction.
 9. The system of claim 1,wherein the multiple models comprises a first subset of models for theone or more signal attenuation parameters for a first frequency band anda second subset of models for the one or more signal attenuationparameters for a second frequency band.
 10. The system of claim 1,wherein the identified subset of models for the UE is selected using oneor more frequency bands at which the UE will communicate with the basestation.
 11. The system of claim 1, wherein the identified subset ofmodels for the UE comprises a first subset of models for the one or moresignal attenuation parameters for a first frequency band and a secondsubset of models for the one or more signal attenuation parameters for asecond frequency band.
 12. The system of claim 1, wherein the multiplemodels comprises at least one statistical model, machine learning model,or both.
 13. A method comprising: obtaining multiple models indicatingone or more signal attenuation parameters, wherein each model is used toestimate values for a corresponding channel transmission parameter at aone or more UEs or a transmitter in communication with the one or moreUEs; receiving, from a UE, a notification to connect to the transmitter;identifying a subset of models from the multiple models for the UE; andtransmitting, to the UE, a packet comprising information of theidentified subset of models.
 14. At least one non-transitorycomputer-readable storage medium storing instructions, which, whenexecuted by at least one data processor of a system, cause the systemto: obtain multiple models indicating one or more signal attenuationparameters, wherein each model is used to estimate values for acorresponding channel transmission parameter at a one or more UEs or atransmitter in communication with the one or more UEs; receive, from aUE, a notification to connect to the transmitter; identify a subset ofmodels from the multiple models for the UE; and transmit, to the UE, apacket comprising information of the identified subset of models. 15.The at least one non-transitory computer-readable storage medium ofclaim 14, wherein the multiple models is stored at a data storagelocation communicatively coupled to the UE, and wherein the UE isconfigured to access the identified subset of models from the datastorage location using the information in the received packet.
 16. Theat least one non-transitory computer-readable storage medium of claim14, wherein the information of the identified subset of models enablesthe UE to modify channel transmission parameter values of communicationbetween the UE and the transmitter.
 17. The at least one non-transitorycomputer-readable storage medium of claim 14, wherein the one or moresignal attenuation parameters comprise: power, signal strength, signalto noise ratio, signal to interference and noise ratio, reference signalreceived power, reference signal strength indication, or any combinationthereof.
 18. The at least one non-transitory computer-readable storagemedium of claim 14, wherein the channel transmission parametercomprises: modulation scheme, multiple-input and multiple-output rank,transmit power level, channel bandwidth, number of carriers, number ofsub-carriers, sub-carrier spacing, or any combination thereof.
 19. Theat least one non-transitory computer-readable storage medium of claim14, wherein the multiple models comprises a first subset of models forthe one or more signal attenuation parameters for a first frequency bandand a second subset of models for the one or more signal attenuationparameters for a second frequency band.
 20. The at least onenon-transitory computer-readable storage medium of claim 14, wherein theidentified subset of models for the UE comprises a first subset ofmodels for the one or more signal attenuation parameters for a firstfrequency band and a second subset of models for the one or more signalattenuation parameters for a second frequency band.